000201794 001__ 201794
000201794 005__ 20190317000009.0
000201794 0247_ $$2doi$$a10.1016/j.engstruct.2014.09.001
000201794 022__ $$a0141-0296
000201794 02470 $$2ISI$$a000346622500019
000201794 037__ $$aARTICLE
000201794 245__ $$aMethodologies for predicting natural frequency variation of a suspension bridge
000201794 260__ $$bElsevier$$c2014$$aOxford
000201794 269__ $$a2014
000201794 300__ $$a11
000201794 336__ $$aJournal Articles
000201794 520__ $$aIn vibration-based structural health monitoring, changes in the natural frequency of a structure are used to identify changes in the structural conditions due to damage and deterioration. However, natural frequency values also vary with changes in environmental factors such as temperature and wind. Therefore, it is important to differentiate between the effects due to environmental variations and those resulting from structural damage. In this paper, this task is accomplished by predicting the natural frequency of a structure using measurements of environmental conditions. Five methodologies - multiple linear regression, artificial neural networks, support vector regression, regression tree and random forest - are implemented to predict the natural frequencies of the Tamar Suspension Bridge (UK) using measurements taken from 3 years of continuous monitoring. The effects of environmental factors and traffic loading on natural frequencies are also evaluated by measuring the relative importance of input variables in regression analysis. Results show that support vector regression and random forest ate the most suitable methods for predicting variations in natural frequencies. In addition, traffic loading and temperature are found to be two important parameters that need to be measured. Results show potential for application to continuously monitored structures that have complex relationships between natural frequencies and parameters such as loading and environmental factors. (C) 2014 Elsevier Ltd. All rights reserved.
000201794 700__ $$0242297$$g186981$$uUniv Warwick, Sch Engn, Coventry CV4 7AL, W Midlands, England$$aLaory, I.
000201794 700__ $$0242301$$g198411$$uEcole Polytech Fed Lausanne, Swiss Fed Inst Technol Lausanne, Appl Appl Comp & Mech Lab, CH-1015 Lausanne, Switzerland$$aTrinh, T.
000201794 700__ $$uEcole Polytech Fed Lausanne, Swiss Fed Inst Technol Lausanne, Appl Appl Comp & Mech Lab, CH-1015 Lausanne, Switzerland$$aSmith, Ian F. C.$$0241981$$g106443
000201794 700__ $$aBrownjohn, J. M. W.
000201794 773__ $$j80$$tEngineering Structures$$q221-221
000201794 8564_ $$uhttps://infoscience.epfl.ch/record/201794/files/Laory-et-al-Postprint-2014-EStruc.pdf$$zn/a$$s1187749$$yn/a
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000201794 917Z8 $$x106443
000201794 937__ $$aEPFL-ARTICLE-201794
000201794 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000201794 980__ $$aARTICLE